RT Journal Article
SR Electronic
T1 Multi-objective Bayesian Optimization with Heuristic Objectives for Biomedical and Molecular Data Analysis Workflows
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.06.08.495370
DO 10.1101/2022.06.08.495370
A1 Alina Selega
A1 Kieran R. Campbell
YR 2022
UL http://biorxiv.org/content/early/2022/06/12/2022.06.08.495370.abstract
AB Many practical applications require optimization of multiple, computationally expensive, and possibly competing objectives that are well-suited for multi-objective Bayesian optimization (MOBO) procedures. However, for many types of biomedical data, measures of data analysis workflow success are often heuristic and therefore it is not known a priori which objectives are useful. Thus, MOBO methods that return the full Pareto front may be suboptimal in these cases. Here we propose a novel MOBO method that adaptively updates the scalarization function using properties of the posterior of a multi-output Gaussian process surrogate function. This approach selects useful objectives based on a flexible set of desirable criteria, allowing the functional form of each objective to guide optimization. We demonstrate the qualitative behaviour of our method on toy data and perform proof-of-concept analyses of single-cell RNA sequencing and highly multiplexed imaging datasets.Competing Interest StatementThe authors have declared no competing interest.